Finding experiments

To use incense we first have to instantiate an experiment loader that will enable us to query the database for specific runs.

QuerySet([Experiment(id=17, name=autoencoder_test), Experiment(id=18, name=autoencoder_test), Experiment(id=19, name=autoencoder_test), Experiment(id=20, name=autoencoder_test), Experiment(id=21, name=autoencoder_test), Experiment(id=22, name=autoencoder_test), Experiment(id=23, name=autoencoder_test), Experiment(id=24, name=autoencoder_test), Experiment(id=74, name=autoencoder_test), Experiment(id=75, name=autoencoder_test)])
pmap({'epochs': 250, 'iteration': False, 'targets_type': 'Mnist', 'batch_size': 256, 'seed': 25619811, 'autoencoder_type': 'Over_dim_iteration'})
targets_type iteration autoencoder_type batch_size
exp_id
17 Mnist False Over_dim_iteration 256
18 Mnist False Over_dim_iteration 128
19 Mnist False Over_dim_iteration 64
20 Mnist False Over_dim_iteration 32
21 10_Targets False Over_dim_iteration 256
22 10_Targets False Over_dim_iteration 128
23 10_Targets False Over_dim_iteration 64
24 10_Targets False Over_dim_iteration 32
74 Noisy False Over_dim_iteration 256
75 Noisy False Over_dim_iteration 128
QuerySet([Experiment(id=17, name=autoencoder_test), Experiment(id=18, name=autoencoder_test), Experiment(id=19, name=autoencoder_test), Experiment(id=20, name=autoencoder_test), Experiment(id=21, name=autoencoder_test), Experiment(id=22, name=autoencoder_test), Experiment(id=23, name=autoencoder_test), Experiment(id=24, name=autoencoder_test), Experiment(id=74, name=autoencoder_test), Experiment(id=75, name=autoencoder_test)])
predictions_df_0
predictions_df_10
predictions_df_20
predictions_df_30
predictions_df_40
predictions_df_50
predictions_df_60
predictions_df_70
predictions_df_80
predictions_df_90
predictions_df_100